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  1. Metamaterials present great potential in the applications of solar cells and nanophotonics, such as super lenses and other meta devices, owing to their superior optical properties. In particular, hyperbolic metamaterials (HMMs) with exceptional optical anisotropy offer improved manipulation of light–matter interactions as well as a divergence in the density of states and thus show enhanced performances in related fields. Recently, the emerging field of oxide–metal vertically aligned nanocomposites (VANs) suggests a new approach to realize HMMs with flexible microstructural modulations. In this work, a new oxide–metal metamaterial system, CeO 2 –Au, has been demonstrated with variable Au phase morphologies from nanoparticle-in-matrix (PIM), nanoantenna-in-matrix, to VAN. The effective morphology tuning through deposition background pressure, and the corresponding highly tunable optical performance of three distinctive morphologies, were systematically explored and analyzed. A hyperbolic dispersion at high wavelength has been confirmed in the nano-antenna CeO 2 –Au thin film, proving this system as a promising candidate for HMM applications. More interestingly, a new and abnormal in-plane epitaxy of Au nanopillars following the large mismatched CeO 2 matrix instead of the well-matched SrTiO 3 substrate, was discovered. Additionally, the tilting angle of Au nanopillars, α , has been found to be a quantitative measure of the balance between kinetics and thermodynamics during the depositions of VANs. All these findings provide valuable information in the understanding of the VAN formation mechanisms and related morphology tuning. 
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    Free, publicly-accessible full text available July 31, 2024
  2. Oxide-metal-based hybrid materials have gained great research interest in recent years owing to their potential for multifunctionality, property coupling, and tunability. Specifically, oxide-metal hybrid materials in a vertically aligned nanocomposite (VAN) form could produce pronounced anisotropic physical properties, e.g. , hyperbolic optical properties. Herein, self-assembled HfO 2 -Au nanocomposites with ultra-fine vertically aligned Au nanopillars (as fine as 3 nm in diameter) embedded in a HfO 2 matrix were fabricated using a one-step self-assembly process. The film crystallinity and pillar uniformity can be obviously improved by adding an ultra-thin TiN-Au buffer layer during the growth. The HfO 2 -Au hybrid VAN films show an obvious plasmonic resonance at 480 nm, which is much lower than the typical plasmonic resonance wavelength of Au nanostructures, and is attributed to the well-aligned ultra-fine Au nanopillars. Coupled with the broad hyperbolic dispersion ranging from 1050 nm to 1800 nm in wavelength, and unique dielectric HfO 2 , this nanoscale hybrid plasmonic metamaterial presents strong potential for the design of future integrated optical and electronic switching devices. 
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  3. Transition metal nitrides such as titanium nitride (TiN) possess exceptional mechanical-, chemical-, and thermal-stability and have been utilized in a wide variety of applications ranging from super-hard, corrosion-resistive, and decorative coatings to nanoscale diffusion barriers in semiconductor devices. Despite the ongoing interest in these robust materials, there have been limited reports focused on engineering high-aspect ratio TiN-based nanocomposites with anisotropic magnetic and optical properties. To this end, we explored TiN–Fe thin films with self-assembled vertical structures integrated on Si substrates. We showed that the key physical properties of the individual components (e.g., ferromagnetism from Fe) are preserved, that vertical nanostructures promote anisotropic behavior, and interactions between TiN and Fe enable a special magneto-optical response. This TiN–Fe nanocomposite system presents a new group of complex multifunctional hybrid materials that can be integrated on Si for future Si-based memory, optical, and biocompatible devices. 
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  4. We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs. Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities. We propose a framework using mixed high-order proximities on dangling-aware entity alignment. Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment. Extensive experiments with two evaluation settings shows that our method more precisely detects dangling entities, and better aligns matchable entities. Further investigations demonstrate that our framework can mitigate the hubness problem on dangling-aware entity alignment. 
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  5. Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As a result, RE still suffers from unintended entity bias, i.e., the spurious correlation between entity mentions (names) and relations. Entity bias can mislead the RE models to extract the relations that do not exist in the text. To combat this issue, some previous work masks the entity mentions to prevent the RE models from over-fitting entity mentions. However, this strategy degrades the RE performance because it loses the semantic information of entities. In this paper, we propose the CoRE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information. We first construct a causal graph for RE, which models the dependencies between variables in RE models. Then, we propose to conduct counterfactual analysis on our causal graph to distill and mitigate the entity bias, that captures the causal effects of specific entity mentions in each instance. Note that our CoRE method is model-agnostic to debias existing RE systems during inference without changing their training processes. Extensive experimental results demonstrate that our CoRE yields significant gains on both effectiveness and generalization for RE. The source code is provided at: https://github.com/vanoracai/CoRE. 
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